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ComputingLifeSci

A complete computational biology curriculum — 23 courses covering programming, databases, machine learning, genomics and research infrastructure. Written for biologists, not computer scientists.

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Programming for Biology

Python for Biologists
R for Biologists
Perl for Biologists
MATLAB for Biologists
SAS for Biologists
BioJava for Biologists

Data & Databases

SQL for Biological Databases
Bioinformatics Databases
Big Data Mining for Biologists

AI & Machine Learning

Machine Learning for Biologists
Artificial Intelligence for Biologists
MLOps for Biologists
AI Tools for the Bioinformatics Lab

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Machine Learning for Biologists82 chapters

Chapter 4.1 — Data Cleaning: Missing Values and Outliers
Chapter 4.2 — Normalization, Standardization, Encoding, and Scaling
Chapter 4.3 — Feature Engineering and Feature Selection
Chapter 4.4 — Dimensionality Reduction: PCA, t-SNE, and UMAP

/Machine Learning for Biologists/PART 4 — Data Preprocessing

Chapter 15 of 826 min read

Chapter 4.2 — Normalization, Standardization, Encoding, and Scaling

1. Learning Objectives Understand why features need to be put on comparable scales Distinguish normalization, standardization, and encoding Apply these transformations in R and Python 2. Theory Most ML algorithms are…

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PreviousChapter 4.1 — Data Cleaning: Missing Values and Outliers NextChapter 4.3 — Feature Engineering and Feature Selection